Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Predicting navigation patterns on the mobile-internet using time of the week
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Analysis of topic dynamics in web search
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
The sum of its parts: reducing sparsity in click estimation with query segments
Information Retrieval
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Machine learning for predicting user clicks in Web-based search offers automated explanation of user activity. We address click prediction in the Web search scenario by introducing a method for click prediction based on observations of past queries and the clicked documents. Due to the sparsity of the problem space, commonly encountered when learning for Web search, new approaches to learn the probabilistic relationship between documents and queries are proposed. Two probabilistic models are developed, which differ in the interpretation of the query-document co-occurrences. A novel technique, namely, conditional probability hierarchy, flexibly adjusts the level of granularity in parsing queries, and, as a result, leverages the advantages of both models.